Jamal Zraqou | Machine Learning | Research Excellence Award

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Assoc. Prof. Dr. Jamal Zraqou | Machine Learning | Research Excellence Award

Associate Professor | University of Petra | Jordan

Assoc. Prof. Dr. Jamal S. Zraqou is an active researcher with demonstrated contributions across data-driven engineering, machine learning, cybersecurity, and digital transformation. He has authored 45 scholarly documents indexed in Scopus, accumulating 202 citations with an h-index of 9, reflecting consistent academic impact. His recent work addresses optimization techniques for engineering design, advanced machine learning methods for phishing detection, cybersecurity vulnerability analysis, and the strategic role of business intelligence in digital transformation. Dr. Zraqou has collaborated with a broad international network of over 60 co-authors, highlighting interdisciplinary and cross-sector engagement. His research supports practical problem-solving in engineering systems, information security, and decision intelligence, contributing to improved technological resilience, safer digital environments, and enhanced organizational competitiveness at societal and industrial levels.

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Featured Publications

Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

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Prof. Dr. Francesco Inchingolo | Artificial Intelligence | Research Excellence Award

Professor in Odontostomatological Diseases (Scientific Sector MED/28) | University of Bari “Aldo Moro” – University Hospital “Policlinico di Bari” | Italy

Prof. Dr. Francesco Inchingolo is a leading Italian clinician-scientist in oral and maxillofacial sciences, renowned for his multidisciplinary contributions spanning dentistry, oral surgery, orthodontics, implantology, regenerative medicine, and public health. A Full Professor of Odontostomatological Diseases and long-standing director of major specialization programs, he has significantly advanced clinical training and translational research at the University of Bari “Aldo Moro.” His global scholarly impact is exemplified by 12,137 citations from 6,038 documents, 489 publications, and an h-index of 66, demonstrating sustained excellence and international recognition. A Principal Investigator in multiple funded projects, he has driven innovation in stem-cell applications, platelet-derived biomaterials, piezosurgery, bone regeneration, orthodontic biomechanics, geriatric dentistry, pediatric oral care, and complex maxillofacial pathologies. His extensive editorial and reviewer roles, together with collaborations across Europe, Asia, and the United States, emphasize his position as a central figure in global dental research networks. Prof. Inchingolo has delivered numerous invited lectures worldwide and serves as Visiting Professor in several international institutions, strengthening academic exchanges and capacity building. His work has been recognized through an exceptional series of national and international distinctions—including the prestigious Sant’Apollonia Award, multiple CDUO, SIDO, and SIOH honors, international research incentive awards, and cultural and scientific excellence recognitions such as the Carthage 2.0 Prize, “Tribute to Life,” and best research poster awards across diverse dental disciplines. Through his high-impact publications, clinical innovations, and leadership in advanced oral surgery and implantology programs, he has contributed substantially to improving patient outcomes, advancing therapeutic technologies, and shaping modern dental and maxillofacial practice on a global scale.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

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Prof. Dr. Ahmed Hamza Osman Ahmed | Data Science | Best Researcher Award

Professor of Computer Science | King Abdulaziz University | Saudi Arabia

Prof. Dr. Ahmed Hamza Osman Ahmed is a distinguished computer scientist and cybersecurity expert whose research bridges artificial intelligence, information security, and data privacy. With over 70 peer-reviewed publications in prestigious journals such as IEEE, Elsevier, and Springer, his scholarly impact is evidenced by 709 citations across 658 documents and an h-index of 14, underscoring his significant contributions to the field. His research encompasses AI-driven cybersecurity systems, intrusion detection, digital forensics, and blockchain-based data integrity, with several funded projects advancing intelligent threat prediction and misinformation detection. Prof. Ahmed has played a pivotal role in developing ABET-aligned curricula, integrating machine learning into cybersecurity education, and supervising more than 25 postgraduate theses in cybersecurity and data science. Internationally recognized for academic excellence, he has received awards such as the Gold Medal at PECIPTA 2011 and Best Postgraduate Student at Universiti Teknologi Malaysia. His extensive collaborations across Saudi Arabia, Malaysia, and Sudan reflect his commitment to fostering global research partnerships and advancing secure, AI-empowered digital ecosystems. Through his leadership in teaching, research, and academic service, Prof. Ahmed continues to contribute to shaping the future of cybersecurity and artificial intelligence with profound educational and societal impact.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. Elssied, N. O. F., Ibrahim, O., & Osman, A. H. (2014). A novel feature selection based on one-way ANOVA F-test for e-mail spam classification. Research Journal of Applied Sciences, Engineering and Technology, 7(3), 625–638. Citations: 223

  2. Elhadi, A. A. E., Maarof, M. A., & Osman, A. H. (2012). Malware detection based on hybrid signature behaviour application programming interface call graph. American Journal of Applied Sciences, 9(3), 283–293. Citations: 137

  3. Osman, A. H., Salim, N., Binwahlan, M. S., Alteeb, R., & Abuobieda, A. (2012). An improved plagiarism detection scheme based on semantic role labeling. Applied Soft Computing, 12(5), 1493–1502. Citations: 128

  4. Osman, A. H., & Aljahdali, H. M. (2020). An effective ensemble boosting learning method for breast cancer virtual screening using neural network model. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2976149 Citations: 93

  5. Abuobieda, A., Salim, N., Albaham, A. T., Osman, A. H., & Kumar, Y. J. (2012). Text summarization features selection method using pseudo genetic-based model. In Proceedings of the 2012 International Conference on Information Retrieval & Knowledge Management (pp. 84–89). Citations: 84